High-Throughput Steady-State Enzyme Kinetics Measured in a Parallel Droplet Generation and Absorbance Detection Platform

Microfluidic water-in-oil emulsion droplets are becoming a mainstay of experimental biology, where they replace the classical test tube. In most applications, such as ultrahigh-throughput directed evolution, the droplet content is identical for all compartmentalized assay reactions. When emulsion droplets are used for kinetics or other functional assays, though, concentration dependencies of initial rates that define Michaelis–Menten parameters are required. Droplet-on-demand systems satisfy this need, but extracting large amounts of data is challenging. Here, we introduce a multiplexed droplet absorbance detector, which—coupled to semi-automated droplet generation—forms a tubing-based droplet-on-demand system able to generate and extract quantitative datasets from defined concentration gradients across multiple series of droplets for multiple time points. The emergence of a product is detected by reading the absorbance of the droplet sets at multiple, adjustable time points by reversing the flow direction after each detection, so that the droplets pass a line scan camera multiple times. Detection multiplexing allows absorbance values at 12 distinct positions to be measured, and enzyme kinetics are recorded for label-free concentration gradients that are composed of about 60 droplets each, covering as many concentrations. With a throughput of around 8640 data points per hour, a 10-fold improvement compared to the previously reported single point detection method is achieved. In a single experiment, 12 full datasets of high-resolution and high-accuracy Michaelis–Menten kinetics were determined to demonstrate the potential for enzyme characterization for glycosidase substrates covering a range in enzymatic hydrolysis of 7 orders of magnitude in kcat/KM. The straightforward setup, high throughput, excellent data quality, and wide dynamic range that allows coverage of diverse activities suggest that this system may serve as a miniaturized spectrophotometer for detailed analysis of clones emerging from large-scale combinatorial experiments.


Data analysis to extract Michaelis-Menten kinetics from line camera data
The raw data read from the line camera as droplets pass through each detection point was converted into Michaelis-Menten plots using the following steps (see the github page https://github.com/fhlab/Line_detector_kinetics for code): The time points corresponding to the end point of each gradient (i.e. time tag for the final droplet in the gradient) were identified visually by a gap that corresponds to the stopping of the flow after substrate injection. We subtracted the duration for substrate injection from these time points in order to calculate the actual start times of the gradients. Furthermore, we entered the number of droplets composing the gradient and overall gradient duration per detection point manually. Although these steps could be automated, it was found to be helpful in the following cases: − Imperfect monodispersity at the end of gradients (after transfer of the droplet maker to an oil well) leading to some larger droplets to not be counted.
− Two droplets getting too close within the tubing and identified as a single drop, altering the overall droplet number.
− Small differences seen between actual gradient duration (30 seconds) and duration read during the measurements due to local flow rate fluctuations, typically ±1 second.

II.
Based on the input gradient times, droplet boundaries were identified from variations in the standard deviation of the moving average of the signal. By using the moving average, sudden signal spikes at the water-oil interfaces (i.e. spikes going above and below the signal corresponding to the actual droplet) are filtered out. This prevents incorrect droplet identification when the signal of the gradient is not monotonic due to the water-oil transitions.

III.
After subtraction of the enzyme-only signal baseline, only points belonging to the gradient droplets were analyzed. Every gradient was fit using the following equation, as expected for second-order kinetics reactions:

Determination of the linear range of the absorbance readout in droplets
We performed a linear data regression to determine the linear region of the calibration gradients, considering that the error in concentration can be assumed as a maximum time shift of 1 s between the estimated and actual start of the gradients ( Figure S1). The resulting calibration curve indicated that linearity was observed up to just above 1 mM pNP. After extracting the slope for the linear region, we obtained the limit of detection (LOD) for each detection point by calculating the standard deviation of the filtered signal level for the enzyme-only baseline. Using a confidence level of 95% (2 sigma above the determined background signal), we deduced that the LOD for every detection point ranged from ⁓5 to 18 µM ( Figure S2).   Initial velocities for the reaction extrapolated from droplet gradient measurements (blue) and fitting functions (red) are plotted for all detection points used to determine the average parameters indicated in Table 1 of the main manuscript. DP: detection point.  Table 1 of the main manuscript. For sample 1 (detection points 1-4) KM was determined with a mean value of 1.6 ± 0.5 mM and kcat with 14.3 ± 1.8 s -1 , for sample 2 (detection points 5-8) KM was 1.8 ± 0.2 mM and kcat 12.1 ± 1.1 s -1 , and for sample 3 KM was 1.5 ± 0.3 mM and kcat 13.7 ± 2.7 s -1 (indicated errors are standard deviations. This results in an overall relative standard deviation of 21% in KM and 15% in kcat across three replicates. DP: detection point.

Figure S9: Individual plots for 12 detection points for the accurate determination of Michaelis-Menten kinetics of SN243 with pNP-β-Xyl in a single experiment.
Detection points 1-4, 5-8 and 9-12 correspond to three distinct substrate concentration gradients. Initial velocities for the reaction extrapolated from droplet gradient measurements (blue) and fitting functions (red) are plotted for all detection points used to determine the average parameters indicated in Table 1 Table 1 of the main manuscript. Fitting the incomplete non-linear Michaelis-Menten curves based on a dataset that has not reached saturation, implies that the errors are larger than those indicated based on averaging and fitting. For sample 1 (detection points 1-4) KM was determined with a mean value of 134.7 ± 33.3 mM and kcat with 3.1 x 10 -2 ± 0.6 x 10 -2 s -1 ; for sample 2 (detection points 5-8) KM was 94.7 ± 42.3 mM and kcat 2.6 x 10 -2 ± 0.8 x 10 -2 s -1 , and for sample 3 KM was 93.9 ± 27.9 mM and kcat 2.5 x 10 -2 ± 0.6 x 10 -2 s -1 (the errors indicated are standard deviations). This results in an overall relative standard deviation of 32% in KM and 25% in kcat across three replicates. Note that due to limited solubility of the substrate in aqueous solution the Michaelis-Menten kinetics shown are extrapolations, and the values for KM and kcat are estimates only, derived from the initial phase of the Michaelis-Menten curve. DP: detection point. S15 Tables   Table S1: Microfluidic systems for kinetic analysis using droplets generated and measured in continuous flow devices.

Supporting
Ref.